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Dry reforming of methane on doped Ni nanoparticles:Featureassisted optimizations and ranking of doping metals for direct activations of CH_(4) and CO_(2)

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Nano Research 2022年第10期15卷 9670-9682页

作者： Shiru Lin Jean-Baptiste Tristan Yang Wang Junwei Lucas Bao Department of Chemistry Boston CollegeChestnut HillMassachusetts 02467USA Department of Computer Science Boston CollegeChestnut HillMassachusetts 02467USA

As a vital energy resource and raw material for many industrial products,syngas(CO and H_(2))is of great *** reforming of methane(DRM)is an important approach to producing syngas(with a hydrogen-to-carbon-monoxide ratio of 1:1 in principle)from methane and carbon dioxide,with a lower operational cost as compared to other reforming ***,many pure metallic catalysts used in DRM face deactivation issues due to coke formation or sintering of the metal particles.A systematic search for highly efficient metallic catalysts,which reduce the reaction barriers for the rate-determining steps and resist carbon deposition,is urgently *** is a typical low-cost transition metal for activating the C–H bond in *** this work,we applied a two-step workflow to search for nickel-based bimetallic catalysts with doping metals M(M-Ni)by combining density functional theory(DFT)calculations and machine learning(ML).We focus on the two critical steps in DRM—CH_(4) and CO_(2) direct *** used DFT and slab models for the Ni(111)facet to explore the relevant reaction pathways and constructed a data set containing structural and energetic information for representative M-Ni *** used this dataset to train ML models with chemical-knowledge-based features and predicted CH_(4) and CO_(2) dissociation energies and barriers,which revealed the composition–activity relationships of the bimetallic *** also used these models to rank the predicted catalytic performance of candidate systems to demonstrate the applicability of ML for catalyst *** emphasized that ML ranking models would be more valuable than regression models in high-throughput ***,we used our trained model to screen 12 unexplored M-Ni systems and showed that the DFT-computed energies and barriers are very close to the ML-predicted values for top candidates,validating the robustness of the trained model.

关键词： Ni-based bimetallic catalysts dry reforming of methane syngas production CH_(4)and CO_(2)direct activations ranking model

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Experimental studies of biomass gasification with air

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Journal of Natural Gas Chemistry 2012年第4期21卷 374-380页

作者： Huili Liu Jianhang Hu Hua Wang Chao Wang Juanqin Li Engineering Research Center of Metallurgical Energy Conservation&Emission Reduction Ministry of EducationFaculty of Metallurgical and Energy EngineeringKunming University of Science and TechnologyKunming 650093YunnanChina

In this work, experimental studies of biomass gasification under different operating conditions were carried out in an updraft gasifier combined with a copper slag reformer. The influence of gasification temperature, equivalence ratio （ER） and copper slag catalyst addition on gas production and tar yield were investigated. The experimental results showed that the content of H2 and CO, gas yield and LHV increased, while the tar yield and the content of CO2, CH4 and C2Hx in the gas product decreased with the temperature. At 800℃, with the increase of ER, the LHV, the tar yield and the content of H2, CO, CH4 and C2H2 in gas products decreased, while the gas yield and the content of CO2 increased. Copper slag was introduced into the secondary reformer for tar decomposition. The Fe3O4 phase in the fresh copper slag was reduced to FeO （Fe^2＋） and metallic Fe by the gas product. Fe species （FeO and metallic Fe） acted as the active sites for tar catalytic decomposition. The catalytic temperature had a significant influence on tar conversion and the composition of the gas product. Typically, the tar conversion of about 17%-54% could be achieved when the catalytic temperature was varied from 750 to 950 ℃. Also, the content of H2 and CO increased with the catalytic temperature, while that of CO2, CH4 and C2Hx in the gas product decreased. It was demonstrated that copper slag was a good catalyst for upgrading the gas product from biomass gasification.

关键词： biomass tar catalyst copper slag syngas production

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Machine learning-driven optimization of plasma-catalytic dry reforming of methane

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Journal of Energy Chemistry 2024年第9期96卷 153-163页

作者： Yuxiang Cai Danhua Mei Yanzhen Chen Annemie Bogaerts Xin Tu Department of Electrical Engineering and Electronics University of LiverpoolLiverpool L693GJUK Research Group PLASMANT Department of ChemistryUniversity of AntwerpUniversiteitsplein 1BE-2610 Wilrijk-AntwerpBelgium College of Electrical Engineering and Control Science Nanjing Tech UniversityNanjing 211816JiangsuChina

This study investigates the dry reformation of methane(DRM)over Ni/Al_(2)O_(3)catalysts in a dielectric barrier discharge(DBD)non-thermal plasma reactor.A novel hybrid machine learning(ML)model is developed to optimize the plasma-catalytic DRM reaction with limited experimental *** address the non-linear and complex nature of the plasma-catalytic DRM process,the hybrid ML model integrates three well-established algorithms:regression trees,support vector regression,and artificial neural networks.A genetic algorithm(GA)is then used to optimize the hyperparameters of each algorithm within the hybrid ML *** ML model achieved excellent agreement with the experimental data,demonstrating its efficacy in accurately predicting and optimizing the DRM *** model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM *** found that the optimal discharge power(20 W),CO_(2)/CH_(4)molar ratio(1.5),and Ni loading(7.8 wt%)resulted in the maximum energy yield at a total flow rate of∼51 mL/***,we investigated the relative significance of each operating parameter on the performance of the plasma-catalytic DRM *** results show that the total flow rate had the greatest influence on the conversion,with a significance exceeding 35%for each output,while the Ni loading had the least impact on the overall reaction *** hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets,enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

关键词： Plasma catalysis Machine learning Process optimization Dry reforming of methane syngas production

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Atomically Dispersed Fe-Co Bimetallic Catalysts for the Promoted Electroreduction of Carbon Dioxide

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Nano-Micro Letters 2022年第2期14卷 79-93页

作者： Zhangsen Chen Gaixia Zhang Yuren Wen Ning Chen Weifeng Chen Tom Regier James Dynes Yi Zheng Shuhui Sun Institut National de la Recherche Scientifique-Énergie Matériaux et Télécommunications VarennesQC J3X 1P7Canada School of Materials Science and Engineering University of Science and Technology100083 BeijingPeople’s Republic of China Canadian Light Source University of SaskatchewanSaskatoonSK S7N 2V3Canada Research Institute of Photocatalysis State Key Laboratory of Photocatalysis on Energy and EnvironmentFuzhou UniversityFuzhou 350116People’s Republic of China

The electroreduction reaction of CO_(2)(ECO_(2)RR)requires high-performance catalysts to convert CO_(2)into useful *** metal-based atomically dispersed catalysts are promising for the high selectivity and activity in ECO_(2)*** work presents a series of atomically dispersed Co,Fe bimetallic catalysts by carbonizing the Fe-introduced Co-zeolitic-imidazolate-framework(C-Fe-Co-ZIF)for the syngas generation from ECO_(2)*** synergistic effect of the bimetallic catalyst promotes CO *** to the pure C-Co-ZiF,C-Fe-Co-ZIF facilitates CO production with a CO Faradaic efficiency(FE)boost of 10%,with optimal FE_(CO)of 51.9%,FE_(H_(2))of 42.4%at-0.55 V,and CO current density of 8.0 mA cm^(-2)at-0.7 V versus reversible hydrogen electrode(RHE).The H_(2)/CO ratio is tunable from 0.8 to 4.2 in a wide potential window of-0.35 to-0.8 V versus *** total FE_(CO+H_(2))maintains as high as 93%over 10 *** proper adding amount of Fe could increase the number of active sites and create mild distortions for the nanoscopic environments of Co and Fe,which is essential for the enhancement of the CO production in ECO_(2)*** positive impacts of Cu-Co and Ni-Co bimetallic catalysts demonstrate the versatility and potential application of the bimetallic strategy for ECO_(2)RR.

关键词： CO_(2)reduction Electrocatalysis syngas production Cobalt Iron Bimetallic catalysts

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